To protect against arbitrary distributional shifts, we frame the problem within the prediction-with-expert-advice paradigm [1]}, [2]}, [3]}.
In this setup, the pACP is thought to be playing a game against “Nature,” who may introduce adaptive or even adversarial distributional shifts.
At each time point, the pACP selects between candidate algorithmic modifications (experts), with the ultimate goal of minimizing the cumulative risk.
Although existing methods like the FixedShare and MarkovHedge have used this framework to continually update algorithms [4]}, [5]}, [6]}, they do not adequately address regulatory needs.
First, these methods are overly conservative in real-world settings and the derived error bounds for realistic time horizons are too wide to provide meaningful error control.
The reason is that these methods are targeted to protect against distributional shifts that are fully adversarial, which are unlikely to occur in practice.
Second, these methods do not control the absolute error—they only control the online error relative to the best sequence of modifications, which could be arbitrarily poor.
